• The KIPS Transactions:PartC
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• v.11C no.2
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• pp.253-262
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• 2004

In this paper, a session allocation algorithm for a switch with multiple shared links is proposed. The algorithm guarantees the reserved bandwidth to each service class and keeps the delay of sessions belonging to a service class as close as possible even if the sessionsare allocated to different shared links. To support these qualities of services, a new scheduling model for multiple shared links is defined and a session allocation algorithm to decide a shared link to be allocated to a new session on the connection establishmentis developed based on the model. The proposed heuristic algorithm allocates a session to a link including the subclass with the shortest (expected) delay that subclasses of the service class the session belongs to will experience. Simulation results verify that a switch with multiple shared links hiring the proposed algorithm provides service classes with fairer bandwidth allocation and higher throughput, and guarantees reserved bandwidth better than the switch hiring other session algorithms. It also guarantees very similarservice delay to the sessions in the same service class.

• The Journal of the Korea Contents Association
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• v.7 no.4
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• pp.10-19
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• 2007

In this paper, Class-1 Air-interface protocols of EPCglobal applied to RFID system in UHF band are analyzed, and the standard anticollision algorithms are realized. Also, the improved anticollision algorithms of the Class-1 Generation-1 and Generation-2 protocol are proposed and the performances of anticollision algorithms are compared. As the results, reduction ratio of total tag recognition time of the improved Generation-1 algorithm is 54.5% for 100 tags and 63.4% for 1000 tags with respect to standard algorithm, respectively. And the reduction ratio of the improved Generation-2 algorithm is 7.9% for 100 tags and 11.7% for 1000 tags. Total recognition times of the improved algorithms are shorter than those of standard algorithms according to increasing the number of tag. Therefore, the improved anticollision algorithm proposed in this paper is the advanced method improving the performance of tag recognition in the RFID system.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.40 no.12
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• pp.46-58
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• 2003

The recent Internet is changing from its best-effort service and needs the network structure and traffic engineering, which can support various applications requiring differentiated traffic processing and high quality of service. The extension of the Internet from wired to wireless system generating location-dependent and burst errors make more difficult to support a good service with the existing packet scheduling algorithm. In this paper, we propose a wireless diffserv packet scheduling (WDSPS) algorithm that can provide reliable service in the differentiated wireless internet service network. The proposed scheduling algorithm solves the HOL(head of line) blocking problems of class queue occurred in wireless network, supports the differentiated service for each class which is defined in differentiated service networks and makes possible the gradual and efficient service compensation not only among classes but also among flows to prevent the monopoly of one class or one flow. Through simulations, we show that our proposed WDSPS scheduling algorithm can provide the required QoS differentiation between classes and enhance the service throughput in various wireless network conditions.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.6
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• pp.35-46
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• 2011

In this paper, we proposed the method of emotion recognition using staged classification model and Fisher's linear discriminant. By organizing the staged classification model, the proposed method improves the classification rate on the Fisher's feature space with high complexity. The staged classification model is achieved by the successive combining of binary classification model which has simple structure and high performance. On each stage, it forms Fisher's linear discriminant according to the two groups which contain each emotion class, and generates the binary classification model by using Adaboost method on the Fisher's space. Whole learning process is repeatedly performed until all the separations of emotion classes are finished. In experimental results, the proposed method provides about 72% classification rate on 8 classes of emotion and about 93% classification rate on specific 3 classes of emotion.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.793-799
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• 2004

Many researches and validations for the complexity metrics of the object-oriented systems have been studied. Most of them are aimed for the measurement of the partial aspects of the systems, for example, the coupling between objects, the complexity of inheritance structures, the cohesion of methods, and so on. But the software practitioners want to measure the complexity of overall system, not partial. We studied the complexity of the overall structures of object-oriented systems by analyzing the class diagram of UML. The class diagram is composed of classes and their relations. There are three kinds of relations, association, generalization, and aggregation, which are making the structure of object-oriented systems to be difficult to understand. We proposed a heuristic metric to measure the complexity of object-oriented systems by putting together the three kinds of the relations. This metric will be helpful to the software developers for their designing tasks by evaluating the complexity of the structures of object-oriented system and redesigning tasks of the system.

• Journal of Internet Computing and Services
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• v.15 no.1
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• pp.63-71
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• 2014

Currently many non-traditional application areas such as artificial intelligence and web databases require advanced modeling power than the existing relational data model. In those application areas, object-oriented database (OODB) is better data model since an OODB can providemodeling power as grouping similar objects into class, and organizing all classes into a hierarchy where a subclass inherits all definitions from its superclasses. The purpose of this paper is to develop an OODB concurrency control scheme dealing with multiple inheritance. The proposed scheme, called Multiple Inheritance Implicit Locking (MIIL), is based on so-called implicit locking. In the proposed scheme, we eliminate redundant locks that are necessary in the existing implicit locking scheme. Intention locks are required as the existing implicit locking scheme. In this paper, it is shown that MIIL has less locking overhead than implicit locking does. We use only OODB inheritance hierarchies, single inheritance and multiple inheritance so that no additional overhead is necessary for reducing locking overhead.

• Journal of KIISE
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• v.44 no.7
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• pp.686-691
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• 2017

Telemarketing has become the center of marketing action of the industry in the information society. Recently, machine learning has emerged in many areas, especially, financial prediction. Financial data consists of lots of unlabeled data in most parts, and therefore, it is difficult for humans to perform their labeling. In this paper, we propose a fusion method of semi-supervised learning for automatic labeling of unlabeled data to predict telemarketing. Specifically, we integrate labeling results of label propagation and co-training with a decision tree. The data with lower reliabilities are removed, and the data are extracted that have consistent label from two labeling methods. After adding them to the training set, a decision tree is learned with all of them. To confirm the usefulness of the proposed method, we conduct the experiments with a real telemarketing dataset in a Portugal bank. Accuracy of the proposed method is 83.39%, which is 1.82% higher than that of the conventional method, and precision of the proposed method is 19.37%, which is 2.67% higher than that of the conventional method. As a result, we have shown that the proposed method has a better performance as assessed by the t-test.

• Journal of Internet Computing and Services
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• v.1 no.1
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• pp.105-113
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• 2000

The representation of a procedural program cannot be applied directly to object oriented program representation consisting of class, object, inheritance, and dynamic binding. Furthermore, preexisting program dependence represented the dependence among statements, but not among variables. That is, it could not solve the problem of which variables make an effect on given variables. Consequently, this study presents the method dependence model representing implementation level information including the dependence among variables in an object oriented program. I also propose implementation-based class testing technique based on the test adequacy criterion of an object-oriented program. Considering inter-data member dependences and a set of axioms for test data adequacy, it generates sequences of methods as test cases which satisfy a flow graph-based testing criterion. For a derived class testing, it considers inheritance relationship and the resuability of the testing information for its parent classes which verified the reduction of test cost through the experiment.

• Journal of KIISE:Software and Applications
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• v.33 no.10
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• pp.886-895
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• 2006

Fingerprint classification reduces the number of matches required in automated fingerprint identification systems by categorizing fingerprints into a predefined class. Support vector machines (SVMs), widely used in pattern classification, have produced a high accuracy rate when performing fingerprint classification. In order to effectively apply SVMs to multi-class fingerprint classification systems, we propose a novel method in which SVMs are generated with the one-vs-all (OVA) scheme and dynamically ordered with $na{\ddot{i}}ve$ Bayes classifiers. More specifically, it uses representative fingerprint features such as the FingerCode, singularities and pseudo ridges to train the OVA SVMs and $na{\ddot{i}}ve$ Bayes classifiers. The proposed method has been validated on the NIST-4 database and produced a classification accuracy of 90.8% for 5-class classification. Especially, it has effectively managed tie problems usually occurred in applying OVA SVMs to multi-class classification.

• Journal of KIISE:Software and Applications
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• v.32 no.11
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• pp.1136-1146
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• 2005

Fingerprint classification is useful in an automated fingerprint identification system (AFIS) to reduce the matching time by categorizing fingerprints. Based on Henry system that classifies fingerprints into S classes, various techniques such as neural networks and support vector machines (SVMs) have been widely used to classify fingerprints. Especially, SVMs of high classification performance have been actively investigated. Since the SVM is binary classifier, we propose a novel classifier-combination model, multiple decision templates (MuDTs), to classily fingerprints. The method extracts several clusters of different characteristics from samples of a class and constructs a suitable combination model to overcome the restriction of the single model, which may be subject to the ambiguous images. With the experimental results of the proposed on the FingerCodes extracted from NIST Database4 for the five-class and four-class problems, we have achieved a classification accuracy of $90.4\%\;and\;94.9\%\;with\;1.8\%$ rejection, respectively.